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Module-Level AI Literacy Integration

Attribution

Original work: "Educators' guide to multimodal learning and Generative AI" — Tünde Varga-Atkins, Samuel Saunders, et al. (2024/25) — CC BY-NC 4.0
Adapted for UK Nursing Education by: Lincoln Gombedza, RN (LD)
Last Updated: December 2025

Integrating AI literacy into individual modules ensures students develop competencies progressively and contextually. This page provides practical guidance for module leaders.

Module Design Principles​

1. Alignment with Learning Outcomes​

Explicit AI Literacy Outcomes

  • Include AI competencies in module learning outcomes
  • Align with NMC proficiencies
  • Map to programme-level competencies
  • Ensure progressive development
  • Make expectations clear

Example Learning Outcomes

  • "Critically evaluate AI-generated clinical information against evidence-based sources"
  • "Use AI tools ethically and safely in care planning while maintaining professional accountability"
  • "Demonstrate understanding of AI limitations in nursing practice"

2. Contextual Integration​

Embed in Clinical Context

  • Relate AI use to module content
  • Use nursing-specific examples
  • Connect to practice scenarios
  • Align with placement learning
  • Ensure clinical relevance

Avoid

  • Generic AI training disconnected from nursing
  • Technology for technology's sake
  • Unrealistic or impractical applications
  • Ignoring clinical context

Module Planning Framework​

Step 1: Needs Analysis​

Assess Current State

  • What AI tools do students already use?
  • What misconceptions exist?
  • What competencies are needed?
  • What resources are available?
  • What are the risks?

Define Module-Specific Needs

  • Which AI competencies align with this module?
  • How can AI enhance learning?
  • What are the ethical considerations?
  • What support do students need?
  • How will competency be assessed?

Step 2: Learning Design​

Select Appropriate AI Applications

  • Care planning assistance
  • Patient education material creation
  • Clinical scenario generation
  • Literature review support
  • Concept explanation

Design Learning Activities

  • Guided AI exploration
  • Critical evaluation exercises
  • Comparative analysis (AI vs. traditional)
  • Ethical case discussions
  • Reflective practice

Example Activity Sequence

  1. Week 1: Introduction to AI in [module topic]
  2. Week 2: Guided practice with AI tool
  3. Week 3: Critical evaluation exercise
  4. Week 4: AI-enhanced assignment
  5. Week 5: Reflection and peer discussion

Step 3: Assessment Design​

AI-Aware Assessment

  • Clarify AI use policy for each assessment
  • Design tasks that require human insight
  • Include process as well as product
  • Require justification and reflection
  • Use varied assessment methods

Assessment Types

  • AI-Enhanced: Students may use AI with disclosure
  • AI-Assisted: AI allowed for specific components only
  • AI-Free: No AI use permitted
  • AI-Focused: Evaluating AI literacy itself

Example Module Plans​

Example 1: Adult Nursing - Care Planning Module​

Module Overview

  • Level: Year 2
  • Credits: 20
  • Focus: Holistic care planning

AI Literacy Integration

Learning Outcomes

  1. Develop evidence-based care plans using appropriate tools including AI
  2. Critically evaluate AI-generated care recommendations
  3. Demonstrate ethical AI use in care planning

Activities

  • Week 3: Introduction to AI-assisted care planning

    • Demonstrate AI care plan generation
    • Discuss limitations and risks
    • Practice prompt crafting

  • Week 4: Critical Evaluation Workshop

    • Generate AI care plans for case studies
    • Compare with NICE guidelines
    • Identify errors and omissions
    • Discuss clinical safety

  • Week 5: Ethical Considerations

    • Patient confidentiality scenarios
    • Professional accountability discussion
    • NMC Code alignment
    • Documentation requirements

Assessment

  • Care plan portfolio (AI-enhanced)
    • Must include AI-generated draft
    • Critical evaluation of AI output
    • Evidence-based modifications
    • Reflection on AI use
    • Disclosure statement

Success Criteria

  • Appropriate AI tool selection
  • Effective prompt crafting
  • Accurate error identification
  • Evidence-based modifications
  • Ethical practice demonstrated

Example 2: Mental Health Nursing - Therapeutic Communication​

Module Overview

  • Level: Year 2
  • Credits: 15
  • Focus: Communication skills

AI Literacy Integration

Learning Outcomes

  1. Practice therapeutic communication using AI simulation
  2. Evaluate AI limitations in understanding human emotion
  3. Maintain person-centered approach despite AI use

Activities

  • Week 2: AI Role-Play Scenarios

    • Use AI to generate patient scenarios
    • Practice responses
    • Receive AI feedback
    • Discuss limitations

  • Week 4: Empathy and AI

    • Compare AI vs. human responses
    • Analyze emotional intelligence gaps
    • Discuss irreplaceable human skills
    • Reflect on therapeutic relationship

  • Week 6: Ethical Practice

    • Patient consent for AI use
    • Privacy in digital communication
    • Professional boundaries
    • Documentation standards

Assessment

  • Communication portfolio (AI-assisted)
    • AI-generated scenarios (disclosed)
    • Video recorded responses
    • Self-evaluation
    • Peer feedback
    • Reflection on AI's role

Example 3: Child Nursing - Health Promotion​

Module Overview

  • Level: Year 3
  • Credits: 20
  • Focus: Child and family health promotion

AI Literacy Integration

Learning Outcomes

  1. Create age-appropriate health education materials using AI
  2. Evaluate AI-generated content for developmental appropriateness
  3. Adapt AI outputs for diverse family needs

Activities

  • Week 3: AI for Health Education

    • Generate patient information leaflets
    • Create visual aids
    • Develop activity sheets
    • Adapt for different ages

  • Week 5: Critical Evaluation

    • Assess developmental appropriateness
    • Check accuracy against evidence
    • Evaluate cultural sensitivity
    • Test with families (simulated)

  • Week 7: Personalization

    • Adapt for learning disabilities
    • Translate for non-English speakers
    • Modify for different health literacy levels
    • Ensure inclusivity

Assessment

  • Health promotion resource pack (AI-enhanced)
    • AI-generated materials (disclosed)
    • Evidence-based modifications
    • Developmental justification
    • Family feedback (simulated)
    • Critical reflection

Implementation Guidance​

For Module Leaders​

Preparation

  • Review institutional AI policy
  • Explore relevant AI tools
  • Identify integration opportunities
  • Design AI-aware assessments
  • Prepare student guidance
  • Plan staff development

Communication

  • Include AI policy in module handbook
  • Discuss in first session
  • Provide written examples
  • Clarify assessment expectations
  • Offer ongoing support
  • Address student concerns

Support

  • Provide AI tool access
  • Offer training sessions
  • Create guidance documents
  • Establish help channels
  • Monitor student progress
  • Gather feedback

Common Challenges and Solutions​

Challenge 1: Student Over-Reliance

  • Solution: Design AI-free components
  • Solution: Require process documentation
  • Solution: Include oral assessments
  • Solution: Emphasize independent practice

Challenge 2: Unequal Access

  • Solution: Provide institutional subscriptions
  • Solution: Offer alternative tools
  • Solution: Allow library access
  • Solution: Flexible deadlines

Challenge 3: Academic Misconduct

  • Solution: Clear disclosure requirements
  • Solution: Process-focused assessment
  • Solution: Unique, personalized tasks
  • Solution: Oral defense of work

Challenge 4: Staff Confidence

  • Solution: Peer support networks
  • Solution: Training workshops
  • Solution: Shared resources
  • Solution: Start small, scale gradually

Assessment Strategies​

AI-Enhanced Assessments​

Care Plan Assignment

  • Students may use AI for initial draft
  • Must verify against evidence
  • Require critical evaluation
  • Include reflection on AI use
  • Disclose all AI assistance

Rubric Criteria

  • Appropriate AI tool selection (10%)
  • Effective prompt crafting (10%)
  • Critical evaluation of output (25%)
  • Evidence-based modifications (30%)
  • Professional reflection (15%)
  • Disclosure and integrity (10%)

AI-Free Assessments​

Clinical Simulation

  • Real-time decision-making
  • No technology access
  • Demonstrates independent competence
  • Assesses clinical reasoning
  • Evaluates practical skills

Oral Examination

  • Defend written work
  • Explain reasoning
  • Answer probing questions
  • Demonstrate understanding
  • Show clinical judgment

Hybrid Assessments​

Portfolio

  • AI-enhanced components (disclosed)
  • AI-free reflections
  • Practical demonstrations
  • Peer evaluations
  • Self-assessments

Quality Assurance​

Module Evaluation​

Student Feedback

  • AI integration effectiveness
  • Support adequacy
  • Assessment clarity
  • Learning outcomes achievement
  • Suggestions for improvement

Learning Analytics

  • AI tool usage patterns
  • Assessment performance
  • Engagement metrics
  • Support requests
  • Misconduct incidents

External Review

  • External examiner feedback
  • Professional body alignment
  • Benchmark against sector
  • Continuous improvement
  • Best practice sharing

Next: Explore Programme Strategy for curriculum-wide integration.